Business Intelligence, Data Analytics, and AI
- 2. APAC Business Innovation Summit ASEAN * SAARC
- 3. There has been a lot of talk about connecting business with analytics, and the common perception is that only businesses with big budgets are able to afford this. What's your opinion on this? How important is this for businesses especially in this pandemic situation that we are currently facing?
- 4. Data, Integration, and Analytics Predictions • By 2024, the productivity gap will double between enterprises that deploy ML-enabled data management, integration, and analysis to automate IT and analytics-related tasks and those that do not. • By 2025, 30% of organizations will be using internal or external prediction markets to make important business decisions • By 2022, a third of G2000 companies will have formal data literacy improvement initiatives in place to drive insights at scale, create sustainable trusted relationships, and counter misinformation • By 2023, 70% of G2000 companies will have metrics in place to evaluate value realized from data, with those at more mature stages gaining resource allocation agility and efficiency over competitors
- 7. There has been a lot of talk about connecting business with analytics, and the common perception is that only businesses with big budgets are able to afford this
- 9. Foundational Questioning what AI is and how to apply it Wrong expectations or disappointment Low digitization Basic analytical capabilities Approaching Hopeful on AI and its promise Digitization under way Looking to increase or optimize processes Cautious about disruption Aspirational Experimented and applied AI Mature Emerging Data science and operational capabilityHigh digitization Desires new business models Achieved a data culture Understands model lifecycle and management Building a foundational data architecture Mapping Your AI Maturity
- 10. What opportunities do you think organizations can benefit from an AI powered business intelligence? And for businesses looking to apply this in their organizations how can they solve the technical challenges of implementing business analytics?
- 11. Analytics 3.0 • The Bosch Group - intelligent fleet management, intelligent vehicle- charging infrastructures, intelligent energy management, intelligent security video analysis, and many more. • Schneider Electric - Today it focuses primarily on energy management, including energy optimization, smartgrid management, and building automation • General Electric - With sensors streaming data from turbines, locomotives, jet engines, and medical-imaging devices, GE can determine the most efficient and effective service intervals for those machines • UPS - It captures information on the 16.3 million packages, on average, that it delivers daily, and it receives 39.5 million tracking requests a day. Resolve by a company’s management to compete on analytics not only in the traditional sense (by improving internal business decisions) but also by creating more- valuable products and services.
- 13. Data Strategy Lifecycle Develop the strategy Create the roadmap Change management plan Analytics lifecycle Measurement plan Perspectives • Scope and Purpose – What data will we manage? How much is our data worth? How do we measure success? • Data Collection – Archiving, what data where and when, single source of truth (data lake), integrating data silos • Architecture – Real time vs Batch, data sharing, data management and security, data modelling, visualisation • Insights and Analysis – Data exploration, self-service, collaboration, managing results • Data Governance – Identify data owners, strategic leadership, data stewardship, data lineage, quality, and cost • Access and Security – RBAC, encryption, PII, access processes, audits, regulatory • Retention and SLAs – Data tiers and retention, SLA’s to the business?
- 14. BI and Apps Data-driven Business analytics and reporting Azure Data Services SQL Server Power Platform (Power BI, PowerApps & Microsoft Flow) Saas with AI Immediate Actionable insights with AI Dynamics 365 AI for Sales, Marketing, & Customer Service Insights Workplace Analytics AI “Accelerators” Solution- specific AI services and patterns Custom AI Data science and Deep AI capability Azure Cognitive Services Azure Bot Service Azure Search Open Frameworks Azure Databricks Azure Machine Learning Azure AI Infrastructre Azure IoT Edge The AI Journey – Where to Start AI Industry Accelerators AI Solution Templates AI Analytics Templates AIMaturity AI Capability
- 16. What trends and changes have you noticed in Business Analytics? Can share some best practices in implementing Business Analytics?
- 18. Products and Services Organization Size Industry Country Shell invests in safety with Azure, AI, and machine vision to better protect customers and service champions In the energy industry, Shell manages everything from wells to retail gas stations—44,000 of them. The company works hard to ensure the safety of service champions and customers at its retail sites. Shell is piloting a new cloud-based, deep learning solution built on Microsoft Azure. The solution uses closed-circuit camera footage and Internet of Things technology to automatically identify safety hazards and alert service champions so they can quickly respond and eliminate potential problems. The NetherlandsMining, Oil and Gas86,000 employeesMicrosoft Azure Azure Databricks Azure IoT Edge Azure IoT Hub
- 19. JUST ANALYTICS CONFIDENTIAL Data Driven People & Culture • Critical Thinking – Step outside internal biases to examine facts you think are true • Data-Centrism – Making data the center of decisions • Democratize Data – Assume data is for everyone, not just privileged few in the organization • Continuous Learning – Move away from “know it all” to “learn it all”. Do not punish failure Business Intelligence Data Engineers Data Scientists
- 21. JUST ANALYTICS CONFIDENTIAL • Embarked on iterative approach to become data driven • Between 2018 Q4 and 2020 Q1, transformed Sales, Inventory, Supply Chain, Finance, Production, Raw Material, Marketing • Reduced operational and management reporting timelines by 80% and FTE by 50% • Piloting AI based sales forecast, balanced with human inputs Data-Centrism
- 22. JUST ANALYTICS CONFIDENTIAL • Data Driven Culture innovation – Critical & Design Thinking training for business users – IT responsible for data availability only – Centralized CoE to provide guidance, support and best practice – Experimentation encouraged • Individual business units and business users driving modern consumption of data • Decision making times reduced by ~ 65%. Top 5 business units seen KPIs increase by ~ 35%. CriticalThinking&ContinuousLearning Leading global semiconductor process manufacturer
- 23. How is Business intelligence important for Security and Compliance of Business?
- 24. HOW TO ENABLE A SECURE SOLUTION
- 25. How can businesses start? How simple or complex? How can they likewise measure ROI?
- 26. JUST ANALYTICS CONFIDENTIAL THE VIA MODEL V I Any Use Case A Value What is the question, business problem or target outcome? How is value realized? Information What data or data sources are involved? Analytics What analytical or data science methods are applied to the data? Fostering Data Literacy and Information as a Second Language, Gartner 2018
- 27. JUST ANALYTICS CONFIDENTIAL THE VIA MODEL: A Real-Life Example V I Any Use Case A Value Business Value = Maximize customer revenue/share of wallet. Cross-sell and upsell. Profitability. Customer Value = enhanced personalization & experience with advice on selection and recommendations Information Shopping history, browsing history, shopping cart items, ratings and reviews, comments, demographics, social media history Analytics Base: Recommendation engine based on affinity analysis & market basket analysis Stretch: Pricing algorithm, campaign analytics, customer profitability optimization models Fostering Data Literacy and Information as a Second Language, Gartner 2018
- 28. JUST ANALYTICS CONFIDENTIAL Recommended Approach Discovery •Pre-Requisite •Defined business question •Activity •½ day workshop to understand •Data Landscape •Current Architecture •Pain Points & Business Areas •Outcome: Defined problem statement •Value: Defined completely •Information: Exploratory •Analytics: Undefined Accelerate •Pre-requisite •Defined problem statement with data domain identified •Activity •2-3-week activity focused on Tech only •AI focused: •Build an AutoML model to validate potential and deploy it •BI Focused: •Building a sample data model with real data to showcase consumption •Outcome: Project viability and business case support •Value: Defined completely •Information: Defined completely •Analytics: Established Deep Dive •Pre-requisite •Viability established with a prototype •Activity •1-3-month activity covering Tech, People & Process •AI focused: •Increase the performance using multiple algorithms and deep learning •MLOps: Automated drift detection and retraining •ML Interpretability •BI Focused: •Operationalizing single subject area with automated pipeline and data quality and self-service model •Outcome: Productionized solution •Value: Defined completely •Information: Defined completely •Analytics: Defined completely